Summary of Spanish and Llm Benchmarks: Is Mmlu Lost in Translation?, by Irene Plaza et al.
Spanish and LLM Benchmarks: is MMLU Lost in Translation?
by Irene Plaza, Nina Melero, Cristina del Pozo, Javier Conde, Pedro Reviriego, Marina Mayor-Rocher, María Grandury
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an evaluation framework for Large Language Models (LLMs) in non-English languages, addressing the limitations of existing benchmarks that rely on automated translation. By translating and running the MMLU benchmark in Spanish using Azure Translator and ChatGPT4, the authors identify test items with inconsistent results between English and Spanish. Manual analysis reveals a significant fraction of these inconsistencies can be attributed to errors in automatic translation. The findings emphasize the importance of adapting benchmarks to target languages, rather than simply relying on automated translation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how we evaluate big language models when they’re used in different languages around the world. Right now, most evaluations just translate an English test into another language using a computer program. But this can be flawed because the quality of the translation affects the results. The authors of this paper take the well-known MMLU benchmark and translate it into Spanish, then run it through a model called ChatGPT4. They look at which questions produce different answers in English and Spanish, and find that many of these differences are due to mistakes in the translation. This shows that we need to do better when evaluating language models in other languages – either by improving our translations or creating new tests that take into account the unique characteristics of each language. |
Keywords
» Artificial intelligence » Translation
